{"ID":5346726,"CreatedAt":"2026-06-30T04:09:55.830587294Z","UpdatedAt":"2026-07-02T14:28:49.359749133Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30380","arxiv_id":"2606.30380","title":"RenderFormer++: Scalable and Physically Grounded Feed-Forward Neural Rendering","abstract":"We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes. Existing Transformer-based neural rendering methods such as RenderFormer achieve promising cross-scene generalization, but suffer from limited physical consistency and poor scalability due to the quadratic attention complexity of triangle-level tokenization. To address these issues, we introduce Physics-Informed Transport Guidance (PITG), which embeds rendering-equation inductive biases into the attention mechanism and enforces transport consistency loss, enabling physically consistent light transport modeling. We further propose Hierarchical Object-Centric Tokenization (HOCT), which aggregates triangle-level features into compact object-level tokens via cross-attention with learnable queries, substantially reducing computational and memory costs while preserving geometric and radiometric information. Extensive experiments demonstrate that RenderFormer++ achieves scalable, stable, and generalizable feed-forward global illumination rendering across complex large-scale scenes with improved physical accuracy and efficiency over prior neural rendering methods.","short_abstract":"We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes. Existing Transformer-based neural rendering methods such as RenderFormer achieve promising cross-scene generalization, but suffer from limited physical consistency and poor scala...","url_abs":"https://arxiv.org/abs/2606.30380","url_pdf":"https://arxiv.org/pdf/2606.30380v1","authors":"[\"Huangsheng Du\",\"Haoran Zhu\",\"Youcheng Cai\",\"Jinyang Meng\",\"Ligang Liu\"]","published":"2026-06-29T14:39:17Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.CV\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}